This research paper investigates the transformative influence of machine learning (ML) on software development life cycle (SDLC) models, illustrating a profound shift from traditional practices. Conventional SDLC models, characterized by their sequential and phase-driven nature, typically involve distinct stages such as requirements analysis, design, implementation, testing, and maintenance. The integration of ML into these models introduces a more dynamic and iterative approach, where machine learning algorithms continuously refine and optimize the development process based on real-time data and feedback. This integration enhances predictive accuracy, automates decision-making processes, and fosters adaptive system performance, addressing the complexities of modern software requirements. The paper concludes that the incorporation of ML into SDLC models not only revolutionizes software development practices but also significantly improves efficiency, responsiveness, and innovation, marking a critical evolution in meeting contemporary technological demands.